Lung segmentation is a step in post-processing of digital chest X-ray images (digital chest radiographs). The segmentation results may influence subsequence detection and analysis of lesions in lung. However, tissues of the chest may overlap with each other and affect the segmentation results. In addition, more X-ray may be absorbed in regions with higher density. Hence, ribs (a high density region) may absorb more X-ray and produce a bright region in a digital chest radiograph, while lungs (a low density region) may absorb less X-ray, and produce a dark region in a digital chest radiograph. Because of the uneven density distribution in the tissues in the chest, classic lung segmentation may create zigzag boundaries. Also, lower corners of a lung may not be segmented by classic lung segmentation.
Two types of approaches may normally be used for lung segmentation. The first type may be based on a rule level, such as a threshold segmentation approach, a region growing approach, an edge detection approach, a morphological filtering approach, etc. Due to the low quality of X-ray images, those approaches of this type may not be used for accurate lung segmentation. The second type may be based on pixel classification, such as a genetic algorithms approach, a neural networks approach, and a fuzzy clustering approach, etc.
In addition, ribs in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities, such as nodules, and may cause these abnormalities to be unidentified, it is therefore beneficial to segment the ribs in chest radiographs and remove the ribs therefrom.
Segmentation of a rib in a digital chest radiograph may be performed using a learning-based approach, such as a neural networks approach, iterative contextual pixel classification (ICPC), etc. However, these approaches are mainly used for classifying bone area and non-bone area, and thus, it may need to train a classifier of this type with a plenty of different images in which ribs may be manually identified.